Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction

Inbarani, H. Hannah; Azar, Ahmad Taher; Mathiyazhagan, Bagyamathi

doi:10.4018/ijskd.290657

Cited by 7 publications

(4 citation statements)

References 167 publications

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“…The Rough Set (RS) theory can be used as a technique to cope with ambiguity and uncertainty in datasets. The majority of applications for rough sets are centered on classification issues (Inbarani et al, 2022(Inbarani et al, , 2020(Inbarani et al, , 2018(Inbarani et al, , 2015a(Inbarani et al, ,b, 2014aJothi et al, 2022Jothi et al, , 2020Jothi et al, , 2019aJothi et al, ,b, 2017Jothi et al, , 2016Jothi et al, , 2013. Data dependencies are the backbone of attribute reduction.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Novel Architecture for Image Classification Based on Rough Set

Nivetha

Inbarani

2023

International Journal of Service Science, Management, Engineering, and Technology

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The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Novel Architecture for Image Classification Based on Rough Set

Nivetha

Inbarani

2023

International Journal of Service Science, Management, Engineering, and Technology

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“…It also provides information on how well the model is able to The performance evaluation of a multi-class classification model is crucial in identifying its effectiveness in solving a problem. Among the most common metrics used for this purpose are accuracy, precision, recall, and F1_Score (Jothi et al, 2022;Lavanya et al, 2022;Inbarani et al, 2022). These measures help to assess the model's overall performance and can identify specific areas that require improvement.…”

Section: Performance Evaluationmentioning

confidence: 99%

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Azar

Tounsi

Fati

et al. 2023

International Journal of Sociotechnology and Knowledge Development

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Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.

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“…Deep learning technology, which evolved from Artificial Neural Networks (ANN), has become a major issue in the computer world and is widely used in fields such as healthcare, image identification, text analytics, cybersecurity, and many more (Dudekula et al, 2023;Fati et al, 2022;Boulmaiz et al, 2022, Zaidi et al 2022Ganesan et al, 2022;Abbas et al, 2022;Azar et al, 2021a,b;Ibrahim et al, 2020;Ramadan et al, 2022;Aslam et al, 2021). Machine Learning (ML) is an artificial intelligence subset that generates dynamic algorithms capable of making data-driven judgments (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Inbarani et al, 2022Inbarani et al, , 2020Inbarani et al, , 2018Inbarani et al, , 2015aInbarani et al, ,b, 2014aFekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Malek et al, 2015a,b;…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Nivetha

Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

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Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction

Cited by 7 publications

References 167 publications

Novel Architecture for Image Classification Based on Rough Set

Novel Architecture for Image Classification Based on Rough Set

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

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